Biologically inspired structure learning with reverse knowledge distillation for spiking neural networks

Spiking neural networks (SNNs) have superb characteristics in sensory information recognition tasks due to their biological plausibility. However, the performance of some current spiking-based models is limited by their structures which means either fully connected or too-deep structures bring too much redundancy. This redundancy from both connection and neurons is one of the key factors hindering the practical application of SNNs. Although Some pruning methods were proposed to tackle this problem, they normally ignored the fact the neural topology in the human brain could be adjusted dynamically. Inspired by this, this paper proposed an evolutionary-based structure construction method for constructing more reasonable SNNs. By integrating the knowledge distillation and connection pruning method, the synaptic connections in SNNs can be optimized dynamically to reach an optimal state. As a result, the structure of SNNs could not only absorb knowledge from the teacher model but also search for deep but sparse network topology. Experimental results on CIFAR100 and DVS-Gesture show that the proposed structure learning method can get pretty well performance while reducing the connection redundancy. The proposed method explores a novel dynamical way for structure learning from scratch in SNNs which could build a bridge to close the gap between deep learning and bio-inspired neural dynamics

Efficient Structure Slimming for Spiking Neural Networks

Spiking neural networks (SNNs) are deeply inspired by biological neural information systems. Compared to convolutional neural networks (CNNs), SNNs are low power consumption because of their spike based information processing mechanism. However, most of the current structures of SNNs are fully-connected or converted from deep CNNs which poses redundancy connections. While the structure and topology in human brain systems are sparse and efficient. This paper aims at taking full advantage of sparse structure and low power consumption which lie in human brain and proposed efficient structure slimming methods. Inspired by the development of biological neural network structures, this paper designed types of structure slimming methods including neuron pruning and channel pruning. In addition to pruning, this paper also considers the growth and development of the nervous system. Through iterative application of the proposed neural pruning and rewiring algorithms, experimental evaluations on CIFAR-10, CIFAR-100, and DVS-Gesture datasets demonstrate the effectiveness of the structure slimming methods. When the parameter count is reduced to only about 10% of the original, the performance decreases by less than 1%

Machine Learning for Actionable Warning Identification: A Comprehensive Survey

Actionable Warning Identification (AWI) plays a crucial role in improving the usability of static code analyzers. With recent advances in Machine Learning (ML), various approaches have been proposed to incorporate ML techniques into AWI. These ML-based AWI approaches, benefiting from ML's strong ability to learn subtle and previously unseen patterns from historical data, have demonstrated superior performance. However, a comprehensive overview of these approaches is missing, which could hinder researchers/practitioners from understanding the current process and discovering potential for future improvement in the ML-based AWI community. In this paper, we systematically review the state-of-the-art ML-based AWI approaches. First, we employ a meticulous survey methodology and gather 50 primary studies from 2000/01/01 to 2023/09/01. Then, we outline the typical ML-based AWI workflow, including warning dataset preparation, preprocessing, AWI model construction, and evaluation stages. In such a workflow, we categorize ML-based AWI approaches based on the warning output format. Besides, we analyze the techniques used in each stage, along with their strengths, weaknesses, and distribution. Finally, we provide practical research directions for future ML-based AWI approaches, focusing on aspects like data improvement (e.g., enhancing the warning labeling strategy) and model exploration (e.g., exploring large language models for AWI)

Genetic analysis of Wnt/PCP genes in neural tube defects

Abstract Background Mouse homozygous mutants in Wnt/planar cell polarity (PCP) pathway genes have been shown to cause neural tube defects (NTDs) through the disruption of normal morphogenetic processes critical to neural tube closure (NTC). Knockout mice that are heterozygotes of single PCP genes likely fail to produce NTD phenotypes, yet damaging variants detected in human NTDs are almost always heterozygous, suggesting that other deleterious interacting variants are likely to be present. Nonetheless, the Wnt/PCP pathway remains a genetic hotspot. Addressing these issues is essential for understanding the genetic etiology of human NTDs. Methods We performed targeted next-generation sequencing (NGS) on 30 NTD-predisposing Wnt/PCP pathway genes in 184 Chinese NTD cases. We subsequently replicated our findings for the CELSR1 gene in an independent cohort of 292 Caucasian NTD samples from the USA. Functional validations were confirmed using in vitro assays. Results CELSR1, CELSR2 and CELSR3 genes were significantly clustered with rare driver coding mutations (q-value< 0.05) demonstrated by OncodriveCLUST. During the validation stage, the number of rare loss of function (LoF) variants in CELSR1 was significantly enriched in NTDs compared with the LoF counts in the ExAC database (p < 0.001). Functional studies indicated compound heterozygote variants of CELSR2 p.Thr2026Met and DVL3 p.Asp403Asn result in down regulation of PCP signals. Conclusions These data indicate rare damaging variants of the CELSR genes, identified in ~ 14% of NTD cases, are expected to be driver genes in the Wnt/PCP pathway. Compound damaging variants of CELSR genes and other Wnt/PCP genes, which were observed in 3.3% of the studied NTD cohort, are also expected to amplify these effects at the pathway level

Immune-checkpoint inhibitor plus chemotherapy versus conventional chemotherapy for first-line treatment in advanced non-small cell lung carcinoma: a systematic review and meta-analysis

Abstract Background Immune-checkpoint inhibitors plus chemotherapy are emerging as effective first-line treatment in advanced non-small-cell lung carcinoma (NSCLC), but little is known about the magnitude of benefits and potential clinical predictors. Methods We performed a meta-analysis of randomized trials that compared PD-1/PD-L1 inhibitor plus chemotherapy with chemotherapy in first line of treatment for advanced NSCLC. The outcomes included progression-free survival (PFS), overall survival (OS), objective response rate (ORR) and treatment-related adverse events (AEs). A fixed-effect or random-effects model was adopted depending on between-study heterogeneity. Results Six trials involving 3144 patients were included. PD-1/PD-L1 inhibitor plus chemotherapy was significantly associated with improvement of PFS (hazards ratio [HR], 0.62; 95% CI 0.57–0.67; P < .001), OS (HR, 0.68; 95% CI 0.53–0.87; P = .002) and ORR (relative ratio [RR], 1.56; 95% CI 1.29–1.89; P < .001), irrespective of PD-L1 expression level. The significant predictor(s) for treatment benefit with combination therapy versus chemotherapy alone were PD-L1 expression level for PFS (P < .001); types of checkpoint inhibitor for ORR (P < .001); histology (P = .025), age (P = .038), gender (P < .001), and types of checkpoint inhibitor (P < .001) for OS. In safety analyses, PD-1/PD-L1 inhibitor plus chemotherapy had significantly higher incidence of adverse events (AEs) of grade 3 or higher (RR, 1.14; P = .007), AEs leading to treatment discontinuation (RR, 1.29; P = .022), serious AEs (RR 1.70; P = .006), immune mediated AEs of any grade (RR, 2.37; P < .001), and immune mediated AEs of grade 3 or higher (RR, 3.71; P < .001). Conclusions PD-1/PD-L1 inhibitor plus chemotherapy, compared with chemotherapy, is associated with significantly improved PFS, ORR, and OS in first-line therapy in NSCLC, at the expense of increased treatment-related AEs

Additional file 1: of Genetic analysis of Wnt/PCP genes in neural tube defects

Table S1. Demographic characteristics in NTD cohorts. (DOCX 17 kb

Additional file 2: of Genetic analysis of Wnt/PCP genes in neural tube defects

Figure S1. Most frequent Pfam domains affected by Wnt/PCP genes in human NTD samples. (TIFF 2976 kb

Additional file 3: of Genetic analysis of Wnt/PCP genes in neural tube defects

Figure S2. CELSR2 p.Thr2026Met and DVL3 p.Asp403Asn did not affect the protein subcellular localization. (A) CELSR2 p.Thr2026Met did not affect CELSR2 subcellular localization in HEK293T & MDCKII cells transfected with CELSR2-GFP and CELSR2 (p.Thr2026Met)-GFP expression plasmids. (B) DVL3 p.Asp403Asn did not affect DVL3 subcellular localization in HEK293T. (TIFF 4540 kb